Large-sample results for optimization-based clustering methods |
| |
Authors: | Peter G. Bryant |
| |
Affiliation: | (1) Graduate School of Business Administration, University of Colorado at Denver, 1200 Larimer Street, Campus Box 165, 80217-3364 Denver, CO, USA |
| |
Abstract: | Many common (nonhierarchical) clustering and classification methods are optimization-based methods, in the sense described by Windham (1987) in this Journal. This paper gives some large sample properties for estimates derived by such methods. Under appropriate conditions, such estimates converge with probability one to a limit, and are asymptotically normally distributed around that limiting value. The conditions are satisfied by most of the common examples of optimization-based methods. Prepared for the 2nd International Conference, International Federation of Classification Societies, Charlottesville, VA, 1989. Supported in part by summer research funds, Graduate School of Business Administration, University of Colorado at Denver. |
| |
Keywords: | Classification Clustering Maximum likelihood Asymptotic properties |
本文献已被 SpringerLink 等数据库收录! |
|